这展示了 AI 在软件开发中的极致自动化实践，做开源项目或小团队开发的可以直接参考——100 个 Codex 实例帮你搞定 PR 审查、安全扫描、问题自动修复，省下大量人力。
Peter Steinberger 在 X 上分享了他如何在 OpenClaw 项目中大规模使用 AI 自动化。他每天在云端运行约 100 个 Codex 实例，覆盖代码审查、安全扫描、问题去重、自动修复、性能基准测试、会议监听等场景。这种模式让团队能以极精简的人力高效运作，展示了未来软件开发的趋势——当 token 成本不再成为瓶颈时，AI 可以深度嵌入开发流程的每个环节。
run codex on every commit
run codex on every commit Peter Steinberger 🦞 @steipete People freaking out over my AI spend. What nobody sees: Part of what excites me so much about working on OpenClaw is that I'm trying to answer the question: How would we build software in the future if tokens don't matter? We constant run ~100 codex in the cloud, reviewing every PR, every issue. If a fix on main lands, @clawsweeper will eventually find that 6 month old issue and close it with an exact reference. We run codex on every commit to review for security issues (as it's far too easy to miss). We run codex to de-duplicate issues and find clusters and send reports for the most pressing issues. We have agents that can recreate complex setups, spin up ephemeral crabbox.sh machines, log into e.g. Telegram, make a video and post before/after fix on the PR. There's codex that watch new issues and - if it fits our documented vision well, automatically create a PR of it. (that then another codex reviews) We have codex running that scans comments for spam and blocks people. We have codex instances running that verify performance benchmarks and report regressions into Discord. We have agents that listen on our meetings and proactively start work, e.g. create PRs when we discuss new features while we discuss them. We build clawpatch.ai to split all our projects into functional units to review and find bugs and regresssions. We do the same split for security with Vercel's deepsec and Codex Security to find regressions and vulnerabilities. All that automation allows us to run this project extremely lean. 🔗 View Quoted Tweet 💬 40 🔄 5 ❤️ 289 👀 42099 📊 50 ⚡